pymc3

How can I define a custom likelihood in PyMC3? In PyMC2, I could use @pymc.potential . I tried to use pymc.Potential in PyMC3, however, it seems that boolean operations cannot be applied to the parameters (I get an error like this when I do that). For example, following code does not work: from pymc import * with Model() as model: x = Normal('x', 1, 1) def z(u): if u > 0: #comparisons like this are not supported # if theano.tensor.lt(0,u): this is how comparison should be done return u ** 2 return -u**3 x2 = Potential('x2', z(x)) start = model.test_point h = find_hessian(start) step =

I run into a common problem I'm wondering if someone can help with. I often would like to use pymc3 in two modes: training (i.e. actually running inference on parameters) and evaluation (i.e. using inferred parameters to generate predictions). In general, I'd like a posterior over predictions, not just point-wise estimates (that's part of the benefit of the Bayesian framework, no?). When your training data is fixed, this is typically accomplished by adding simulated variable of a similar form to the observed variable. For example, from pymc3 import * with basic_model: # Priors for unknown

I have some observational data for which I would like to estimate parameters, and I thought it would be a good opportunity to try out PYMC3. My data is structured as a series of records. Each record contains a pair of observations that relate to a fixed one hour period. One observation is the total number of events that occur during the given hour. The other observation is the number of successes within that time period. So, for example, a data point might specify that in a given 1 hour period, there were 1000 events in total, and that of those 1000, 100 were successes. In another time period,

Is it possible to incrementally update a model in pyMC3. I can currently find no information on this. All documentation is always working with a priori known data. But in my understanding, a Bayesian model also means being able to update a belief. Is this possible in pyMC3? Where can I find info in this? Thank you :) Following @ChrisFonnesbeck's advice, I wrote a small tutorial notebook about incremental prior updating. It can be found here: https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/updating_priors.ipynb Basically, you need to wrap your posterior samples in a custom

I failed to fit a method belonging to an instance of a class, as a Deterministic function, with PyMc3. Can you show me how to do that ? For simplicity, my case is summarised below with a simple example. In reality, my constraint is that everything is made through a GUI and actions like ‘find_MAP’ should be inside methods linked to pyqt buttons. I want to fit the function ‘FunctionIWantToFit’ over the data points. Problem, the following code: import numpy as np import pymc3 as pm3 from scipy.interpolate import interp1d import theano.tensor as tt import theano.compile class cprofile: def __init_